Monitoring state of health information for components

ABSTRACT

In one advantageous embodiment, a method is provided for managing data. Information about the plurality of components is identified using data to form a plurality of instances for a plurality of nodes in response to receiving data for a plurality of components. Each node in the plurality of nodes corresponds to a component in the plurality of components. A number of variables having a range of values are formed to form a data structure. Each value in the range of values indicates a point in time at which a change to the information for a node occurs between a first instance and a second instance in the plurality of instances. More than one change between two consecutive instances is absent. The data structure is used to identify probability information about the plurality of components associated with the plurality of nodes.

BACKGROUND INFORMATION

1. Field

The present disclosure relates generally to processing data, and inparticular, to processing state of health information. Still moreparticularly, the present disclosure relates to identifying probabilityinformation for present and current states of health for components.

2. Background

A processing system is a system that is configured to perform a process,such as, for example, a manufacturing process, a maintenance process, awater purification process, a vehicle control process, and/or some othersuitable type of process. Different types of processing systems may bemonitored to identify problems with components in the processingsystems.

For example, many types of processing systems are monitored to identifywhen a process being performed by the processing system should bestopped to prevent an undesired event from occurring. The undesiredevent may be, for example, damage to components within the processingsystem, failure of components within the processing system, and/or othersuitable types of events.

In many situations, a change in the operation of one component may notresult in a failure of the entire system at the time when the operationof the component changes. However, the change in the operation of thecomponent may begin a cascading sequence of failures through a number ofother components in the system. This cascading sequence of failures mayresult in a failure of the entire system. In this manner, a problem withone component in a system may result in a problem with the entire systemover time.

Therefore, it would be advantageous to have a method and apparatus thattakes into account at least some of the issues discussed above, as wellas possibly other issues.

SUMMARY

In one advantageous embodiment, a method is provided for managing data.Information about the plurality of components is identified using datato form a plurality of instances for a plurality of nodes in response toreceiving data for a plurality of components. Each node in the pluralityof nodes corresponds to a component in the plurality of components. Anumber of variables having a range of values are formed to form a datastructure. Each value in the range of values indicates a point in timeat which a change to the information for a node occurs between a firstinstance and a second instance in the plurality of instances. More thanone change between two consecutive instances is absent. The datastructure is used to identify probability information about theplurality of components associated with the plurality of nodes.

In another advantageous embodiment, a computer system comprises astorage device containing program code and a processor unit configuredto execute the program code. The processor unit is configured to executethe program code to identify information about a plurality of componentsusing the data to form a plurality of instances for a plurality of nodesin response to receiving data for the plurality of components. Each nodein the plurality of nodes corresponds to a component in the plurality ofcomponents. The processor unit is configured to execute the program codeto form a number of variables having a range of values. Each value inthe range of values indicates a point in time at which a change to theinformation for a node occurs between a first instance and a secondinstance in the plurality of instances to form a data structure. Morethan one change between two consecutive instances is absent. Theprocessor unit is configured to execute the program code to use the datastructure to identify probability information about the plurality ofcomponents associated with the plurality of nodes.

In yet another advantageous embodiment, a computer program product formanaging data comprises a computer readable storage medium and programcode stored on the computer readable storage medium. Program code ispresent for identifying information about the plurality of componentsusing the data to form a plurality of instances for a plurality of nodesin response to receiving data for a plurality of components. Each nodein the plurality of nodes corresponds to a component in the plurality ofcomponents. Program code is present for forming a number of variableshaving a range of values. Each value in the range of values indicates apoint in time at which a change to the information for a node occursbetween a first instance and a second instance in the plurality ofinstances to form a data structure. More than one change between twoconsecutive instances is absent. Program code is present for using thedata structure to identify probability information about the pluralityof components associated with the plurality of nodes.

In still yet another advantageous embodiment, an apparatus for managinga manufacturing system comprises a monitoring system and a processorunit. The monitoring system is configured to monitor components in themanufacturing system. The components in the manufacturing system thatare monitored are monitored components. The monitoring system isconfigured to generate data for the monitored components. The processorunit is in communication with the monitoring system. The processor unitis configured to receive the data for the monitored components. Theprocessor unit is configured to identify information about the monitoredcomponents using the data, in response to receiving the data for themonitored components, to form a plurality of instances for a pluralityof nodes in which each node in the plurality of nodes corresponds to amonitored component in the monitored components. The processor unit isconfigured to form a number of variables having a range of values, inwhich each value in the range of values indicates a point in time atwhich a change to the information for a node occurs between a firstinstance and a second instance in the plurality of instances to form adata structure. More than one change between two consecutive instancesis absent. The processor unit is configured to identify probabilityinformation about the components in the system. The processor unit isconfigured to initiate an operation to be performed for themanufacturing system based on the probability information.

In one advantageous embodiment, a method is provided for managingcomponents in a manufacturing system. The manufacturing system comprisesmonitoring components in the manufacturing system. The components thatare monitored are monitored components. Data is generated for themonitored components. The data for the monitored components is received.In response to receiving the data, information about the monitoredcomponents is identified using the data to form a plurality of instancesfor a plurality of nodes for the monitored components in which each nodein the plurality of nodes corresponds to a monitored component in themonitored components. A number of variables having a range of values areformed. Each value in the range of values indicates a point in time atwhich a change to the information for a node occurs between a firstinstance and a second instance in the plurality of instances to form adata structure. More than one change between two consecutive instancesis absent. Probability information about the components in themanufacturing system is identified. At least one of present and futurestates for at least one of the monitored components and unmonitoredcomponents in the manufacturing system based on the probabilityinformation is identified. An operation is initiated to be performed onthe manufacturing system in response to identifying the at least one ofthe monitored components and unmonitored components in the manufacturingsystem based on the probability information.

The features, functions, and advantages can be achieved independently invarious embodiments of the present disclosure or may be combined in yetother embodiments in which further details can be seen with reference tothe following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the advantageousembodiments are set forth in the appended claims. The advantageousembodiments, however, as well as a preferred mode of use, furtherobjectives and advantages thereof, will best be understood by referenceto the following detailed description of an advantageous embodiment ofthe present disclosure when read in conjunction with the accompanyingdrawings, wherein:

FIG. 1 is a block diagram of a processing environment in accordance withan advantageous embodiment;

FIG. 2 is data processing system in which advantageous embodiments maybe implemented;

FIG. 3 is an illustration of a first data structure in accordance withan advantageous embodiment;

FIG. 4 is an illustration of a second data structure in accordance withan advantageous embodiment;

FIG. 5 is an illustration of a conditional probability table inaccordance with an advantageous embodiment;

FIG. 6 is an illustration of a flowchart for managing data in accordancewith an advantageous embodiment;

FIG. 7 is an illustration of a flowchart for managing data in accordancewith an advantageous embodiment; and

FIG. 8 is an illustration of a flowchart for monitoring components inaccordance with an advantageous embodiment.

DETAILED DESCRIPTION

The different advantageous embodiments recognize and take into account anumber of different considerations. For example, the differentadvantageous embodiments recognize and take into account that currentlyavailable systems for monitoring and controlling processing systems mayshut down a system when a component in the processing system operates inan undesired manner. For example, the system may be shut down when acomponent in the processing system operates outside of requiredparameters.

The different advantageous embodiments recognize and take into accountthat when a processing system is comprised of a large number ofcomponents and/or complex components, shutting down a system in responseto one component not operating as desired is not always practical oreven possible. Further, if the change in the operation of the componentresults in a cascading sequence of failures, shutting down the systemtoo early may not be practical.

The different advantageous embodiments recognize and take into accountthat knowing when a particular type of failure will occur in response toa change in the operation of one component may provide valuableinformation. For example, a circuit breaker in an aircraft may begin tooperate in an undesired manner. This problem may eventually lead tofailure of a hydraulic pump. Further, the failure of the hydraulic pumpmay result in undesired operation of a flight control surface. Thedifferent advantageous embodiments recognize and take into account thatit may be desirable to identify an expected time for when the hydraulicpump will fail in response to the undesired operation of the circuitbreaker. For example, a pilot of the aircraft may use the identificationof the expected time for the failure of the hydraulic pump to select anarea for the aircraft to land prior to failure of the hydraulic pump.

The different advantageous embodiments recognize and take into accountthat different components in a processing system and the relationshipsbetween the components in the processing system may be monitored usingsensor data obtained about the different components. The differentadvantageous embodiments recognize and take into account that monitoringthe state of every component within a processing system may require moreprocessing resources and time than desired.

Additionally, currently available processes may monitor a portion of thecomponents in a system. The state of the components in the system thatare not monitored may then be inferred. However, the differentadvantageous embodiments recognize and take into account that currentlyavailable algorithms for inferring the states of these monitoredcomponents may require more time than desired. Further, monitoringcomponents within a processing system such that the state of thesecomponents is monitored in a substantially continuous manner over timemay also require more processing resources and time than desired.

The different advantageous embodiments recognize and take into accountthat some currently available processes may use Kalman filtering tomonitor the states of components. However, the different advantageousembodiments recognize and take into account that with the currentlyavailable processes, Kalman filtering may only be used when continuousstate information for the components in the system. In other words, thestate information needs to be a continuous, normally distributedquantity. Further, some currently available processes for monitoringsystems may not take into account more than two states for a component.

The different advantageous embodiments recognize and take into accountthat Bayesian networks may be used to establish models for processingsystems. A Bayesian network may also be referred to as a belief networkor a directed acyclic graphical model. Bayesian networks that modelsequences of variables over time, for example, are known as dynamicBayesian networks. The different advantageous embodiments recognize andtake into account that using a Bayesian network in which each componentin a processing system is represented may require more computations,processing power, and/or time than desired.

Thus, the different advantageous embodiments provide a method andcomputer system for monitoring processing systems. In particular, thedifferent advantageous embodiments provide a method and apparatus formonitoring the states of components within the processing systems.

In one advantageous embodiment, information about a plurality ofcomponents is identified using data to form a plurality of instances fora plurality of nodes in response to receiving the data for the pluralityof components. Each node in the plurality of nodes corresponds to acomponent in the plurality of components and the plurality of nodes isassociated with each other. A number of variables having a range ofvalues are formed in which the number of variables form a datastructure. Each value in the range of values indicates a point in timeat which a change to the information for a node occurs between a firstinstance and a second instance in the plurality of instances. More thanone change between two consecutive instances is absent. The datastructure is used to identify probability information about a pluralityof components associated with the plurality of nodes.

With reference now to the figures and more particularly with referenceto FIG. 1, an illustration of a processing environment is depicted inaccordance with an advantageous embodiment. In these illustrativeexamples, processing environment 100 may include processing system 102and computer system 104.

Processing system 102 may be any system configured to perform a numberof processes. The number of processes may include, for example, withoutlimitation, a manufacturing process, a maintenance process, a waterpurification process, a vehicle control process, an aircraft controlprocess, a surface ship control process, a spacecraft control process,and/or other suitable types of process.

As depicted, processing system 102 includes components 106. Components106 may be, for example, without limitation, parts, software components,hardware components, devices, machines, and/or other suitable types ofcomponents. Components 106 include monitored components 108 andunmonitored components 110.

In these illustrative examples, monitored components 108 may bemonitored by, for example, without limitation, monitoring system 112.Monitoring system 112 may take the form of any system configured toobtain data 114 about monitored components 108.

Data 114 may include, for example, without limitation, at least one ofstate of health information, maintenance data, diagnostic data, sensordata, a file, a report, a log, and other suitable types of information.For example, without limitation, data 114 may include sensor data thatmay be processed to identify the states of monitored components 108.

As one illustrative example, when monitored components 108 take the formof parts and/or devices, monitoring system 112 may take the form ofsensor system 116. Sensor system 116 may include a number of sensorsconfigured to generate data 114 about monitored components 108.

In these depicted examples, monitoring system 112 sends data 114 tocomputer system 104. Data 114 may be sent to computer system 104 usingwireless and/or wired communications links. Computer system 104comprises number of computers 117 in these examples. In theseillustrative examples, computer system 104 is located remote toprocessing system 102. In other illustrative examples, computer system104 may be part of processing system 102.

Data management process 118 is program code that runs on one or more ofnumber of computers 117 in computer system 103. Data management process118 receives data 114 and processes data 114. As depicted, datamanagement process 118 uses data 114 to form first data structure 120.First data structure 120 may take the form of a graphical model, atable, a database, a file, a report, or some other suitable type of datastructure. In these examples, first data structure 120 takes the form ofgraphical model 122. As one illustrative example, graphical model 122may be a dynamic Bayesian network.

In these depicted examples, graphical model 122 includes plurality ofnodes 124 and plurality of points in time 126 for plurality of nodes124. Each node in plurality of nodes 124 corresponds to a monitoredcomponent in monitored components 108. For example, node 125 inplurality of nodes 124 corresponds to monitored component 109 inmonitored components 108.

In these illustrative examples, the nodes in plurality of nodes 124 areassociated with each other. The association between a first node inplurality of nodes 124 and a second node in plurality of nodes 124 takesthe form of causal relationship 164. A causal relationship between thefirst node and the second node indicates, for example, that the state ofthe first node affects the state of the second node.

Plurality of points in time 126 for plurality of nodes 124 are points intime within a selected period of time. For example, plurality of pointsin time 126 may be within a selected period of time during whichmonitored components 108 were monitored.

Additionally, graphical model 122 includes plurality of instances 128.Plurality of instances 128 are formed when information about monitoredcomponents 108 at particular points in plurality of points in time 126is identified. This information may be identified from processing data114 for monitored components 108. For example, this information mayinclude state information, a classification, a set of parameter values,and/or other suitable information identified from processing data 114.

As one illustrative example, data management process 118 identifiesinformation 134 about monitored component 109 at point in time 140 inplurality of points in time 126. Information 134 is then associated withnode 125 in graphical model 122 corresponding to monitored component 109at point in time 140 to form instance 132. For example, information 134may be placed into node 125 in graphical model 122 at point in time 140to form instance 132. In other illustrative examples, information 134may be associated with node 125 at point in time 140 in some othersuitable manner.

Data management process 118 uses first data structure 120 to form seconddata structure 142. Second data structure 142 takes the form oftransformed graphical model 144 in these examples. Transformed graphicalmodel 144 is a transformed version of a dynamic Bayesian network inthese examples.

As depicted, transformed graphical model 144 includes plurality ofvariables 146. Each variable in plurality of variables 146 correspondsto a node in plurality of nodes 124. In these illustrative examples, avariable is formed in transformed graphical model 144 when a change inthe information identified for two consecutive instances occurs.

For example, instance 148 is a subsequent instance to instance 132 thatis formed when data management process 118 identifies information 152about monitored component 109 at point in time 150. Instances are notformed for any points in time between instance 132 for point in time 140and instance 148 for point in time 150. In other words, instance 132 andinstance 148 are consecutive instances.

When information 152 identified for instance 148 is different frominformation 134 identified for instance 132, data management process 118forms variable 154 in plurality of variables 146 for transformedgraphical model 144. Data management process 118 uses assumption 155 toform variable 154.

In these illustrative examples, assumption 155 indicates that more thanone change between two consecutive instances is absent. In other words,only one change in the information identified for a node may be presentbetween two consecutive instances. For example, if no change in theinformation identified for two consecutive instances is present, datamanagement process 118 assumes that no changes in the information arepresent at any points in time in between the two consecutive instances.Similarly, if a change between the information identified for the twoconsecutive instances is present, data management process 118 assumesthat this change is the only change in the information for the points intime between the two consecutive instances.

Variable 154 has range of values 156. In these illustrative examples,each value in range of values 156 indicates a possible number of pointsin time for which information 134 for node 125 stays the same afterpoint in time 140 before changing to information 152 at point in time150. In other illustrative examples, each value in range of values 156may indicate a possible number of points in time after point in time 140at which the change to information 134 occurs.

In these depicted examples, at least a portion of plurality of variables146 may be associated with each other. At least a portion may be one,some, or all of plurality of variables 146. As one illustrative example,a value for a first variable in plurality of variables 146 may affectthe probability that a second variable in plurality of variables 146 hasa particular value. Data management process 118 uses second datastructure 142 to identify this probability for the range of values forthe first variable and the range of values for the second variable.

In some cases, the values of more than one variable may affect theprobability of the value for another variable. Similarly, in othercases, the value of one variable may affect the probabilities of thevalues for more than one variable.

In these depicted examples, data management process 118 identifies thesetypes of probabilities in the form of conditional probability tables160. A conditional probability table identifies the probability of anevent occurring given the occurrence of another event. In theseexamples, each of conditional probability tables 160 identifies theprobability of a variable in plurality of variables 146 having a valuegiven a number of values for a number of variables in plurality ofvariables 146. A number of, as used herein, means one or more. Forexample, a number of variables is one or more variables.

Data management process 118 may use the information provided inconditional probability tables 160 along with policy 162 to identifyprobabilities for the present and/or future states of monitoredcomponents 108 and/or unmonitored components 110. Policy 162 identifiesassociations between components in components 106. In particular, policy162 may identify associations between the components in monitoredcomponents 108 and/or unmonitored components 110.

Further, data management process 118 may use transformed graphical model144 to generate additional types of information. This information may beused to generate status reports for components 106, monitor the healthof processing system 102, perform maintenance on processing system 104,and/or perform other suitable operations.

In particular, conditional probability tables 160 along with policy 162may be used to initiate an operation to be performed on processingsystem 102. Initiating the operation to be performed on processingsystem 102 may include identifying the operation, sending a command to amachine configured to perform the operation, generating a reportidentifying the operation, performing the operation, and/or some othersuitable type of process.

The operation to be performed may be, for example, without limitation,further inspection of particular components within components 106. Theoperation may also be, for example, without limitation, replacement of acomponent, repair of a component, rework of a component, and/or someother suitable type of operation.

The illustration of processing environment 100 in FIG. 1 is not meant toimply physical or architectural limitations to the manner in whichdifferent advantageous embodiments may be implemented. Other componentsin addition to and/or in place of the ones illustrated may be used. Somecomponents may be unnecessary in some advantageous embodiments. Also,the blocks are presented to illustrate some functional components. Oneor more of these blocks may be combined and/or divided into differentblocks when implemented in different advantageous embodiments.

For example, in some illustrative examples, a portion of number ofcomputers 117 may be part of processing system 102, while anotherportion of number of computers 117 may be located remote to processingsystem 102. Additionally, in some illustrative examples, data managementprocess 118 may process data 114 when data 114 is received. In otherillustrative examples, a separate process running on computer system 104may process data 114 and then send the processed data to data managementprocess 118.

In still other illustrative embodiments, monitoring system 112 mayinclude program code configured to monitor software components. Thisprogram code may be configured to generate data 114 and process data 114before sending data 114 to data management process 118. Further, in somecases, both this program code and data management process 118 may be runon computer system 104.

Further, although first data structure 120 and second data structure 142take the form of Bayesian networks in these illustrative examples, thesedata structures may be formed using other suitable types ofprobabilistic graphical models and/or probabilistic data structures.

With reference now to FIG. 2, a diagram of a data processing system isdepicted in accordance with an advantageous embodiment. In thisillustrative example, data processing system 200 is an example of oneimplementation for one or more of number of computers 117 in computersystem 104 in FIG. 1. As depicted, data processing system 200 includescommunications fabric 202, which provides communications betweenprocessor unit 204, memory 206, persistent storage 208, communicationsunit 210, input/output (I/O) unit 212, and display 214.

Processor unit 204 serves to execute instructions for software that maybe loaded into memory 206. Processor unit 204 may be a set of one ormore processors or may be a multi-processor core, depending on theparticular implementation. Further, processor unit 204 may beimplemented using one or more heterogeneous processor systems in which amain processor is present with secondary processors on a single chip. Asanother illustrative example, processor unit 204 may be a symmetricmulti-processor system containing multiple processors of the same type.

Memory 206 and persistent storage 208 are examples of storage devices216. A storage device is any piece of hardware that is capable ofstoring information such as, for example, without limitation, data,program code in functional form, and/or other suitable informationeither on a temporary basis and/or a permanent basis. Memory 206, inthese examples, may be, for example, a random access memory, or anyother suitable volatile or non-volatile storage device. Persistentstorage 208 may take various forms, depending on the particularimplementation. For example, persistent storage 208 may contain one ormore components or devices. For example, persistent storage 208 may be ahard drive, a flash memory, a rewritable optical disk, a rewritablemagnetic tape, or some combination of the above. The medium used bypersistent storage 208 may be removable. For example, a removable harddrive may be used for persistent storage 208.

Communications unit 210, in these examples, provides for communicationwith other data processing systems or devices. In these examples,communications unit 210 is a network interface card. Communications unit210 may provide communications through the use of either or bothphysical and wireless communications links.

Input/output unit 212 allows for the input and output of data with otherdevices that may be connected to data processing system 200. Forexample, input/output unit 212 may provide a connection for user inputthrough a keyboard, a mouse, and/or some other suitable input device.Further, input/output unit 212 may send output to a printer. Display 214provides a mechanism to display information to a user.

Instructions for the operating system, applications, and/or programs maybe located in storage devices 216, which are in communication withprocessor unit 204 through communications fabric 202. In theseillustrative examples, the instructions are in a functional form onpersistent storage 208. These instructions may be loaded into memory 206for execution by processor unit 204. The processes of the differentembodiments may be performed by processor unit 204 using computerimplemented instructions, which may be located in a memory, such asmemory 206.

These instructions are referred to as program code, computer usableprogram code, or computer readable program code that may be read andexecuted by a processor in processor unit 204. The program code, in thedifferent embodiments, may be embodied on different physical or computerreadable storage medium, such as memory 206 or persistent storage 208.

Program code 218 is located in a functional form on computer readablemedium 220 that is selectively removable and may be loaded onto ortransferred to data processing system 200 for execution by processorunit 204. Program code 218 and computer readable medium 220 formcomputer program product 222. In one example, computer readable medium220 may be computer readable storage medium 224 or computer readablesignal medium 226. Computer readable storage medium 224 may include, forexample, an optical or magnetic disc that is inserted or placed into adrive or other device that is part of persistent storage 208 fortransfer onto a storage device, such as a hard drive, that is part ofpersistent storage 208. Computer readable storage medium 224 also maytake the form of a persistent storage, such as a hard drive, a thumbdrive, or a flash memory that is connected to data processing system200. In some instances, computer readable storage medium 224 may not beremovable from data processing system 200.

Alternatively, program code 218 may be transferred to data processingsystem 200 using computer readable signal medium 226. Computer readablesignal medium 226 may be, for example, a propagated data signalcontaining program code 218. For example, computer readable signalmedium 226 may be an electro-magnetic signal, an optical signal, and/orany other suitable type of signal. These signals may be transmitted overcommunications links, such as wireless communications links, an opticalfiber cable, a coaxial cable, a wire, and/or any other suitable type ofcommunications link. In other words, the communications link and/or theconnection may be physical or wireless in the illustrative examples. Thecomputer readable medium also may take the form of non-tangible medium,such as communications links or wireless transmissions containing theprogram code.

In some illustrative embodiments, program code 218 may be downloadedover a network to persistent storage 208 from another device or dataprocessing system through computer readable signal medium 226 for usewithin data processing system 200. For instance, program code stored ina computer readable storage medium in a server data processing systemmay be downloaded over a network from the server to data processingsystem 200. The data processing system providing program code 218 may bea server computer, a client computer, or some other device capable ofstoring and transmitting program code 218.

The different components illustrated for data processing system 200 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. The different advantageousembodiments may be implemented in a data processing system includingcomponents in addition to or in place of those illustrated for dataprocessing system 200. Other components shown in FIG. 2 can be variedfrom the illustrative examples shown.

The different embodiments may be implemented using any hardware deviceor system capable of executing program code. As one example, dataprocessing system 200 may include organic components integrated withinorganic components and/or may be comprised entirely of organiccomponents excluding a human being. For example, a storage device may becomprised of an organic semiconductor.

In another illustrative example, processor unit 204 may take the form ofa hardware unit that has circuits that are manufactured or configuredfor a particular use. This type of hardware may perform operationswithout needing program code to be loaded into a memory from a storagedevice to be configured to perform the operations.

For example, when processor unit 204 takes the form of a hardware unit,processor unit 204 may be a circuit system, an application specificintegrated circuit (ASIC), a programmable logic device, or some othersuitable type of hardware configured to perform a number of operations.With a programmable logic device, the device is configured to performthe number of operations. The device may be reconfigured at a later timeor may be permanently configured to perform the number of operations.

Examples of programmable logic devices include, for example, aprogrammable logic array, programmable array logic, a field programmablelogic array, a field programmable gate array, and other suitablehardware devices. With this type of implementation, program code 218 maybe omitted because the processes for the different embodiments areimplemented in a hardware unit.

In still another illustrative example, processor unit 204 may beimplemented using a combination of processors found in computers andhardware units. Processor unit 204 may have a number of hardware unitsand a number of processors that are configured to run program code 218.With this depicted example, some of the processes may be implemented inthe number of hardware units, while other processes may be implementedin the number of processors.

As another example, a storage device in data processing system 200 isany hardware apparatus that may store data. Memory 206, persistentstorage 208, and computer readable medium 220 are examples of storagedevices in a tangible form.

In another example, a bus system may be used to implement communicationsfabric 202 and may be comprised of one or more buses, such as a systembus or an input/output bus. Of course, the bus system may be implementedusing any suitable type of architecture that provides for a transfer ofdata between different components or devices attached to the bus system.Additionally, a communications unit may include one or more devices usedto transmit and receive data, such as a modem or a network adapter.Further, a memory may be, for example, memory 206 or a cache such asfound in an interface and memory controller hub that may be present incommunications fabric 202.

With reference now to FIG. 3, an illustration of a data structure isdepicted in accordance with an advantageous embodiment. In thisillustrative example, data structure 300 is an example of oneimplementation for first data structure 120 formed by data managementprocess 118 in FIG. 1. Data structure 300 takes the form of graphicalmodel 301 in this depicted example. Further, graphical model 301 is adynamic Bayesian network.

As depicted, graphical model 301 includes node 302, node 304, and node306. Node 302, node 304, and node 306 correspond to monitored componentsin a processing system in this illustrative example. For example, node302, node 304, and node 306 may correspond to monitored components 108in processing system 102 in FIG. 1. In other words, node 302, node 304,and node 306 represent these monitored components in graphical model301.

Further, in this illustrative example, node 302 and node 304 have acausal relationship. In other words, a change in the state of node 302may affect the state of node 304. Similarly, node 304 and node 306 havea causal relationship. A change in the state of node 304 may affect thestate of node 306.

As depicted, graphical model 301 also includes plurality of points intime 308. Plurality of points in time 308 include first point in time310, second point in time 312, third point in time 314, fourth point intime 316, fifth point in time 318, sixth point in time 320, seventhpoint in time 322, eighth point in time 324, ninth point in time 326,and tenth point in time 328.

Additionally, graphical model 301 includes plurality of instances 329.Plurality of instances 329 includes instances 330, 332, 334, 336, 338,340, 342, 344, 346, 348, and 350. Each of plurality of instances 329 isformed when information about a component for a point in time inplurality of points in time 308 is identified and associated with thecorresponding node. In this illustrative example, this information isstate information.

For example, instance 330 is formed when state information about thecomponent associated with node 302 for second point in time 312 isidentified and associated with node 302. This association with node 302is performed by placing the state information into node 302 at secondpoint in time 312 in graphical model 301.

In this illustrative example, the state information identified forinstance 330 indicates that the component corresponding to node 302 ingraphical model 301 has an “b” state at second point in time 312. Thisinformation may be identified by processing data such as, for example,without limitation, data 114 obtained from monitoring system 112 in FIG.1.

This data may be processed by using the data to perform calculations,evaluating the data against a number of criteria, identifying portionsof the data, randomly sampling the data to form observations, and/orperforming other suitable types of operations using the data.

As another illustrative example, instance 346 is formed when stateinformation about the component associated with node 306 for third pointin time 314 is identified. As depicted, the information identified forinstance 346 indicates that the component corresponding to node 306 hada “b” state at third point in time 314.

As depicted, consecutive instances in plurality of instances 329 for anode may be formed for consecutive points in time. Additionally, anumber of points in time for which state information is not identifiedfor the component associated with a node may be present betweenconsecutive instances.

Instance 330 and instance 332 are examples of consecutive instances inthis depicted example. Two points in time are present between instance330 and instance 332 for which state information about the componentassociated with node 302 is not identified. The state informationidentified to form instance 332 indicates that the component associatedwith node 302 has an “a” state at fifth point in time 318.

With reference now to FIG. 4, an illustration of a data structure isdepicted in accordance with an advantageous embodiment. In thisillustrative example, data structure 400 is an example of oneimplementation for second data structure 142 in FIG. 1. Data structure400 takes the form of transformed graphical model 401 in this depictedexample.

Transformed graphical model 401 is a transformed version of graphicalmodel 301 in FIG. 3. Further, transformed graphical model 401 is atransformed Bayesian network. Transformed graphical model 401 is formedby a data management process such as, for example, data managementprocess 118 in FIG. 1.

In this illustrative example, the data management process uses anassumption, such as assumption 155 in FIG. 1 to form transformedgraphical model 401. The assumption, in this depicted example, indicatesthat more than one component state change between two consecutiveinstances in plurality of instances 329 in graphical model 301 in FIG. 3is absent. Using this assumption, the data management process formsplurality of variables 402 for transformed graphical model 401.Plurality of variables 402 are an example of one implementation forplurality of variables 146 in FIG. 4.

A variable in plurality of variables 402 is formed each time that achange in the state information about a component corresponding to anode is present between two consecutive instances. In these examples,the two consecutive instances are not formed for consecutive points intime. In other words, at least one point in time is present between thetwo consecutive instances for which state information about thecomponent corresponding to the node is not identified.

As depicted, plurality of variables 402 in transformed graphical model401 includes variables 404, 406, 408, 410, 412, 414, 416, 418, 420, and422. Each variable in plurality of variables 402 corresponds to one ofnode 302, node 304, and node 306 in FIG. 3. For example, variables 404,406, 408, and 410 correspond to node 302 in FIG. 3. Variables 412, 414,and 416 correspond to node 304 in FIG. 3. Variables 418, 420, and 422correspond to node 306 in FIG. 3.

Further, each of plurality of variables 402 is associated with aninterval of time within the range of plurality of points in time 308.For example, variables 404, 406, 408, 410, 412, 414, 416, 418, 420, and422 are associated with intervals of time 424, 426, 428, 430, 432, 434,436, 438, 440, and 442, respectively.

Each interval of time extends from the point in time for a firstinstance to the point in time for a second instance in which a change inthe state information is present between the first instance and thesecond instance. For example, interval 426 associated with variable 406extends from second point in time 312 to fifth point in time 318 betweenwhich the state information about the component associated with node 302changes.

In this illustrative example, a portion of the intervals of time intransformed graphical model 401 may not begin or end at a point in timein plurality of points in time 308. In other words, an interval of timeassociated with a variable in plurality of variables 402 may have anunknown beginning point in time or an unknown ending point in time. Ifreasoning about a state for which there is no preceding (first) or forwhich there is no following (second) data instance is required, eachpossible value is consecutively hypothesized and a distribution ofpossible values obtained in this manner.

In this depicted example, a beginning point in time for an interval oftime is unknown when an instance for a node is formed at least one pointin time after the first point in time in plurality of points in time308. For example, interval 424 associated with variable 404 has anunknown beginning point in time. An ending point in time for an intervalof time is present when an instance for a node is not formed for thelast point in time in plurality of points in time 308. For example,interval 430 associated with variable 410 has an unknown ending point intime.

Each of the variables in plurality of variables 402 has a range ofvalues identified based on the assumption used by the data managementprocess. Each value in this range of values indicates a number ofpossible points in time for which the state information about thecomponent corresponding to the node stays the same before changing.

As one illustrative example, the state information about the componentcorresponding to node 302 changes between second point in time 312 andfifth point in time 318 as depicted in FIG. 3. The component changesfrom having a “b” state at second point in time 312 to an “a” state atfifth point in time 318, as depicted in FIG. 3. Variable 406 associatedwith this change has a range of values that includes 0, 1, and 2. Forexample, the state information about the component corresponding to node302 may change at substantially third point in time 314 such thatvariable 406 has a value of 0. The state information may change atsubstantially fourth point in time 316 such that variable 406 has avalue of 1. The state information may change at substantially fifthpoint in time 318 such that variable 406 has a value of 2.

Additionally, variable 404 and variable 412 have unknown ranges ofvalues. The range of values for each of these variables is unknownbecause instances prior to first point in time 310 have not been formedin this example. Further, variable 410 and variable 422 also haveunknown ranges of values. The range of values for each of thesevariables is unknown because instances after tenth points in time 328have not been formed in this example.

As depicted, transformed graphical model 401 also includes associations444, 446, 448, 450, 452, 454, and 456. These associations indicate thevariables in plurality of variables 402 for which the values of thevariables may affect the probability of a particular value for anothervariable. For example, the value for variable 406 and the value forvariable 408 may affect the probability that variable 414 will have aparticular value. Further, the value for variable 408 may affect theprobability of the value for 416.

In this illustrative example, the data management process may usetransformed graphical model 401 to generate conditional probabilitytables to identify the probabilities for the different states of thecomponents corresponding to node 302, node 304, and node 306.

With reference now to FIG. 5, an illustration of a conditionalprobability table is depicted in accordance with an advantageousembodiment. In this illustrative example, conditional probability table500 is an example of one implementation of a conditional probabilitytable in conditional probability tables 160 in FIG. 1.

Conditional probability table 500 is an example of a conditionalprobability table that may be generated using transformed graphicalmodel 401 in FIG. 4. In this illustrative example, conditionalprobability table 500 is generated based on the association of variable406 and variable 408 with variable 414. Conditional probability table500 identifies probabilities 502. Each probability in probabilities 502is a probability of variable 414 in FIG. 4 having a particular valuegiven a value for variable 406 in FIG. 4 and a value for variable 408 inFIG. 4.

As depicted, conditional probability table 500 has rows 504 and columns506. Rows 504 include all possible combinations of values for variable406 and variable 408. Columns 506 include all possible values forvariable 414.

In these illustrative examples, probabilities 502 may be generated usingthe following equations:

$\begin{matrix}{{{\overset{\sim}{P}\left( {{{\overset{\sim}{X}}^{t_{0}:t_{1}} = {\left. v \middle| Y^{s_{0}^{1}:s_{1}^{1}} \right. = u^{1}}},\ldots\mspace{14mu},{Y^{s_{0}^{l};s_{1}^{l}} = u^{l}}} \right)} = {\left\lbrack {\prod\limits_{t = t_{0}}^{t_{0} + v}\;{\overset{\_}{\pi}}^{(t)}} \right\rbrack\pi^{({t_{0} + v + 1})}}},} & (1)\end{matrix}$where

$\begin{matrix}{\pi^{(t)} = \left\{ \begin{matrix}{P\left( {{X = {\left. x^{t_{1}} \middle| Y \right. = w}},{X^{prev} = X^{t_{0}}}} \right)} \\{{{if}\mspace{14mu} Y^{t}\mspace{14mu}{is}\mspace{14mu}{observed}\mspace{14mu}{to}\mspace{14mu}{be}\mspace{14mu} w},} \\{P\left( {{X = {\left. x^{t_{1}} \middle| Y \right. = {{pa}\;}^{t}}},\left( {\overset{\sim}{X}}^{t_{0}:t_{1}} \right),{X^{prev} = x^{t_{0}}}} \right)} \\{{if}\mspace{14mu} Y^{t}\mspace{14mu}{is}\mspace{14mu}{unobserved}}\end{matrix} \right.} & (2)\end{matrix}$and analogously

$\begin{matrix}{{\overset{\_}{\pi}}^{(t)} = \left\{ \begin{matrix}{P\left( {{X = {\left. x^{t_{0}} \middle| Y \right. = w}},{X^{prev} = X^{t_{0}}}} \right)} \\{{{if}\mspace{14mu} Y^{t}\mspace{14mu}{is}\mspace{14mu}{observed}\mspace{14mu}{to}\mspace{14mu}{be}\mspace{14mu} w},} \\{P\left( {{X = {\left. x^{t_{0}} \middle| Y \right. = {pa}^{t}}},\left( {\overset{\sim}{X}}^{t_{0}:t_{1}} \right),{X^{prev} = x^{t_{0}}}} \right)} \\{{if}\mspace{14mu} Y^{t}\mspace{14mu}{is}\mspace{14mu}{unobserved}}\end{matrix} \right.} & (3)\end{matrix}$where equation (1) uses standard probabilistic notation.

As one illustrative example, probability 506 in probabilities 502 may becalculated using the above equations, where t0 is 3, t1 is 7 and v is 2.A parent variable is a variable in which the value for the variable hasan effect on the probability of another variable having a particularvalue. For variable 506, the parent variables are identified as X(2:5)and X(5:9). Using the above equations, the multiplication P ⁽³⁾ P ⁽⁴⁾ P⁽⁵⁾P⁽⁶⁾ is calculated, with the “P” probabilities defined by equations(2) and (3).

While a variable may have multiple parent variables, the probability ofa change in the information for the node associated with the variable attime t is determined by the conditional probability table of at most oneparent variable in which s_(j0)<t<s_(j1). This distinguished parent isdenoted by Pa^(t)(X^(t0:t1)) and its value at time t bypa^(t)(X^(t0:t1)) in equation (3).

In addition, for nodes that do not have parents in the second datastructure, the specification of the conditional probability table issimplified toπ^((t)) =P(X=x ^(t) ¹ |X=x ^(t) ⁰ )π ^((t)) =P(x=x ^(t) ⁰ |X=x ^(t) ⁰ )

With reference now to FIG. 6, a flowchart of the process of managingdata is depicted in accordance with an advantageous embodiment. Process600 illustrated in FIG. 6 may be implemented using data managementprocess 118 running on computer system 104 in processing environment 100in FIG. 1.

Process 600 begins by identifying information about a plurality ofcomponents to form a plurality of instances for a plurality of nodes inresponse to receiving data for the plurality of components (operation602). Each node in the plurality of nodes corresponds to a component inthe plurality of components.

Next, process 600 forms a number of variables having range of values toform a data structure (operation 604). Each value in the range of valuesindicates a point in time at which a change to the information for anode occurs between a first instance and a second instance in theplurality of instances.

Thereafter, process 600 uses the data structure to identify probabilityinformation about the plurality of components associated with theplurality of nodes (operation 606), with the process terminatingthereafter.

With reference now to FIG. 7, an illustration of a flowchart of aprocess for managing data is depicted in accordance with an advantageousembodiment. Process 700 illustrated in FIG. 7 may be implemented usingdata management process 118 running on computer system 104 in processingenvironment 100 in FIG. 1.

Process 700 begins by receiving data about a plurality of components(operation 702). The data received in operation 702 may be received froma monitoring system, such as monitoring system 112 in FIG. 1. Themonitoring system monitors the plurality of components such that theplurality of components are monitored components. These monitoredcomponents are part of a processing system. The processing system alsoincludes unmonitored components.

Process 700 then identifies information about the plurality ofcomponents using the data received to form a plurality of instances fora plurality of nodes (operation 704). Each node in the plurality ofnodes corresponds to a component in the plurality of components.

Thereafter, process 700 forms a first data structure using the pluralityof nodes and the plurality of instances (operation 706). The first datastructure may be first data structure 120 in FIG. 1. Further, the firstdata structure may take the form of first data structure 300 in FIG. 3.

Process 700 then forms a number of variables having a range of values(operation 708). Each value in the range of values indicates a point intime at which a change to the information for a node occurs between afirst instance and a second instance in the plurality of instances.

Next, process 700 forms a second data structure using the number ofvariables having the range of values (operation 710). The second datastructure may be, for example, second data structure 142 in FIG. 1.Further, the second data structure may take the form of second datastructure 400 in FIG. 4.

Thereafter, process 700 identifies a number of associations between thenumber of variables (operation 712). Then, process 700 generates anumber of conditional probability tables (operation 714). In thisillustrative example, each conditional probability table identifiesprobabilities for when the information for the node changes between thefirst instance and the second instance based on when the information fora number of nodes in the plurality of nodes changes.

Thereafter, the process uses the probability information in the numberof conditional probability tables and a policy to identify at least oneof present and future states for at least one of the monitoredcomponents and unmonitored components in the processing system(operation 716), with the process terminating thereafter.

With reference now to FIG. 8, an illustration of a flowchart of aprocess for monitoring components in a system is depicted in accordancewith an advantageous embodiment. Process 800 illustrated in FIG. 8 maybe implemented using monitoring system 112 in processing environment 100in FIG. 1.

Process 800 begins by monitoring a plurality of components in aprocessing system over a plurality of points in time (operation 802).Process 800 generates data about the plurality of components (operation804). This data may include, for example, without limitation, state ofhealth information, maintenance data, diagnostic data, sensor data, afile, a report, a log, and/or other suitable types of information.

Thereafter, process 800 sends the data to a computer system forprocessing (operation 806), with the process terminating thereafter. Thecomputer system may be, for example, computer system 104 in FIG. 1.Further, the data may be received by a data management process runningon the computer system. This data may then be processed using process600 illustrated in FIG. 6 and/or process 700 illustrated in FIG. 7.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures.

For example, two blocks shown in succession may, in fact, be executedsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts, orcombinations of special purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The invention can take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In a preferred embodiment, the invention isimplemented in software, which includes but is not limited to firmware,resident software, microcode, etc.

Furthermore, the invention can take the form of a computer programproduct accessible from a computer-usable or computer-readable mediumproviding program code for use by or in connection with a computer orany instruction execution system. For the purposes of this description,a computer-usable or computer readable medium can be any tangibleapparatus that can contain, store, communicate, propagate, or transportthe program for use by or in connection with the instruction executionsystem, apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer-readable medium include asemiconductor or solid state memory, magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk and an optical disk. Current examples of opticaldisks include compact disk—read only memory (CD-ROM), compactdisk—read/write (CD-R/W) and DVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

The description of the different advantageous embodiments has beenpresented for purposes of illustration and description, and is notintended to be exhaustive or limited to the embodiments in the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art. Further, different advantageousembodiments may provide different advantages as compared to otheradvantageous embodiments. The embodiment or embodiments selected arechosen and described in order to best explain the principles of theembodiments, the practical application, and to enable others of ordinaryskill in the art to understand the disclosure for various embodimentswith various modifications as are suited to the particular usecontemplated.

What is claimed is:
 1. A method for managing data, the methodcomprising: responsive to receiving data for a plurality of components,identifying information about the plurality of components using the datato form a plurality of instances for a plurality of nodes in which eachnode in the plurality of nodes corresponds to a component in theplurality of components; forming a number of variables having a range ofvalues, in which each value in the range of values indicates a point intime at which a change to the information for a particular node occursbetween a first instance and a second instance in the plurality ofinstances to form a data structure, wherein more than one change betweentwo consecutive instances is absent; and using the data structure toidentify probability information about the plurality of componentsassociated with the plurality of nodes wherein the probabilityinformation comprises probabilities of when a state of a component X inthe plurality of components changes based on when a number of states fora number of components in the plurality of components changes,calculated such that: each entry in a conditional probability table of amodified network corresponds to a probability that a value of componentX changes precisely at a particular time given that a state ofcomponents on which the state of the component X depends, the parents,changes at a particular combination of times, and is defined as follows:a probability that a change in the state of the component X which isobserved to certainly occur between time t₀ and time t₁ occurs preciselyat time step v, conditioned on the state of the parents changing theirstate precisely at time steps U₁, U₂, . . . , U_(L) is given by aproduct of a number of negative elementary transition probabilities, onefor each time up to and including v, and a positive elementarytransition probability from time t₀ to time t₁ at time step (v+1); suchthat a negative elementary transition probability for time t correspondsto the probability, of a presumed state of the parents failing to inducea state change in component X, and is retrieved from a correspondingconditional probability table of an original Bayesian network datastructure, where the corresponding conditional probability table isdetermined by the state of the parents that the parents would have attime t had the changes in their state occurred precisely at time stepsU₁, U₂, . . . , U_(L) and the state of component X being a value at abeginning of an interval in which the state change in component X isknown to have occurred; and such that a positive elementary transitionprobability for time t corresponds to the probability of the presumedstate of the parents, inducing the change in the state of component X,and is retrieved from the corresponding conditional probability table ofthe original Bayesian network data structure, where the correspondingconditional probability table is determined by the state of the parentsthat the parents would have at time t had the changes in their stateoccurred precisely at time steps U₁, U₂, . . . , U_(L) and the state ofcomponent X being the value at an end of an interval in which the changeis known to have occurred.
 2. The method of claim 1 further comprising:monitoring the plurality of components in a system using a monitoringsystem, wherein the monitoring system is configured to generate the datafor a plurality of points in time, and wherein the plurality ofcomponents are monitored components and components in the system thatare not monitored are unmonitored components.
 3. The method of claim 2,wherein the change to the information for the particular node is achange to at least one of: state information, health information,maintenance data, diagnostic data, sensor data, a file, a report, and alog for a component to which the node corresponds.
 4. The method ofclaim 2, wherein the probability information includes probabilities ofwhen states of the unmonitored components change based on when changesto states of the monitored components occur and further comprising:using the probability information and a policy to identify at least oneof present and future states for at least one of the monitoredcomponents and unmonitored components in the system.
 5. The method ofclaim 4 further comprising: initiating an operation to perform on thesystem based on an identification of the at least one of the present andfuture states for the at least one of the monitored components andunmonitored components in the system.
 6. The method of claim 2, whereinthe system is configured to perform a process selected from one of amanufacturing process, a maintenance process, a water purificationprocess, a vehicle control process, an aircraft control process, asurface ship control process, and a spacecraft control process.
 7. Themethod of claim 1 further comprising: forming a first data structureusing the plurality of nodes and the plurality of instances; and formingthe data structure using the number of variables having the range ofvalues, wherein the data structure is a second data structure.
 8. Themethod of claim 7, wherein the step of using the second data structureto identify the probability information about the plurality ofcomponents associated with the plurality of nodes comprises: generatinga number of conditional probability tables in which each of the numberof conditional probability tables identifies probabilities for when theinformation for the particular node changes between the first instanceand the second instance based on when information for a number of nodesin the plurality of nodes changes.
 9. The method of claim 7, wherein thefirst data structure is a graphical model and wherein the step offorming the first data structure using the plurality of nodes and theplurality of instances comprises: identifying a number of causalrelationships between pairs of nodes in the plurality of nodes; andforming the graphical model using the plurality of nodes, the pluralityof instances, and the number of causal relationships.
 10. The method ofclaim 9, wherein the graphical model is a dynamic Bayesian network. 11.The method of claim 7, wherein the second data structure is atransformed graphical model and wherein the step of forming the seconddata structure using the number of variables having the range of valuescomprises: identifying a number of associations between the number ofvariables in which an association in the number of associationsindicates that a first value of a first variable in the number ofvariables affects a probability of a second value of a second variablein the number of variables.
 12. The method of claim 11, wherein thetransformed graphical model is a transformed dynamic Bayesian network.13. The method of claim 1, wherein the data comprises at least one ofstate of health information, maintenance data, diagnostic data, sensordata, a file, a report, and a log.
 14. A computer system comprising: astorage device containing program code; a processor unit configured toexecute the program code to identify information about a plurality ofcomponents using data to form a plurality of instances for a pluralityof nodes in response to receiving data for the plurality of componentsin which each node in the plurality of nodes corresponds to a componentin the plurality of components; form a number of variables having arange of values, in which each value in the range of values indicates apoint in time at which a change to the information for a particular nodeoccurs between a first instance and a second instance in the pluralityof instances to form a data structure, wherein more than one changebetween two consecutive instances is absent; and use the data structureto identify probability information about the plurality of componentsassociated with the plurality of nodes, wherein the probabilityinformation is calculated such that: each entry in a conditionalprobability table of a modified network corresponds to a probabilitythat a value of component X changes precisely at a particular time giventhat a state of components on which the state of the component Xdepends, the parents, changes at a particular combination of times, andis defined as follows: a probability that a change in the state of thecomponent X which is observed to certainly occur between time t₀ andtime t₁ occurs precisely at time step v, conditioned on the state of theparents changing their state precisely at time steps U₁, U₂, . . . ,U_(L) is given by a product of a number of negative elementarytransition probabilities, one for each time up to and including v, and apositive elementary transition probability from time t₀ to time t₁ attime step (v+1); such that a negative elementary transition probabilityfor time t corresponds to the probability, of a presumed state of theparents failing to induce a state change in component X, and isretrieved from a corresponding conditional probability table of anoriginal Bayesian network data structure, where the correspondingconditional probability table is determined by the state of the parentsthat the parents would have at time t had the changes in their stateoccurred precisely at time steps U₁, U₂, . . . , U_(L) and the state ofcomponent X being a value at a beginning of an interval in which thestate change in component X is known to have occurred; and such that apositive elementary transition probability for time t corresponds to theprobability of the presumed state of the parents, inducing the change inthe state of component X, and is retrieved from the correspondingconditional probability table of the original Bayesian network datastructure, where the corresponding conditional probability table isdetermined by the state of the parents that the parents would have attime t had the changes in their state occurred precisely at time stepsU₁, U₂, . . . , U_(L) and the state of component X being the value at anend of an interval in which the change is known to have occurred. 15.The computer system of claim 14, wherein the change to the informationfor the particular node is a change to at least one of: stateinformation, health information, maintenance data, diagnostic data,sensor data, a file, a report, and a log for a component to which thenode corresponds.
 16. The computer system of claim 14, wherein theprocessor unit is further configured to execute the program to code toform a first data structure using the plurality of nodes and theplurality of instances; and form the data structure using the number ofvariables having the range of values, wherein the data structure is asecond data structure.
 17. The computer system of claim 16, wherein inbeing configured to execute the program code to use the second datastructure to identify the probability information about the plurality ofcomponents associated with the plurality of nodes, the processor unit isconfigured to execute the program code to generate a number ofconditional probability tables in which each of the number ofconditional probability tables identifies probabilities for when theinformation for the particular node changes between the first instanceand the second instance based on when information for a number of nodesin the plurality of nodes changes.
 18. The computer system of claim 17,wherein the second data structure is a transformed graphical model andwherein in being configured to execute the program code to form thesecond data structure using the number of variables having the range ofvalues, the processor unit is configured to execute the program code toidentify a number of associations between the number of variables inwhich an association in the number of associations indicates that afirst value of a first variable in the number of variables affects aprobability of a second value of a second variable in the number ofvariables.
 19. The computer system of claim 18, wherein the transformedgraphical model is a transformed dynamic Bayesian network.
 20. Thecomputer system of claim 16, wherein the first data structure is agraphical model and wherein in being configured to execute the programcode to form the first data structure using the plurality of nodes andthe plurality of instances, the processor unit is configured to identifya number of causal relationships between pairs of nodes in the pluralityof nodes; and form the graphical model using the plurality of nodes, theplurality of instances, and the number of causal relationships.
 21. Thecomputer system of claim 20, wherein the graphical model is a dynamicBayesian network.
 22. The computer system of claim 14, wherein theprobability information identified using the data structure comprisesprobabilities of when a state for the component in the plurality ofcomponents changes based on when a number of states for a number ofcomponents in the plurality of components changes.
 23. A computerprogram product for managing data comprising: a non-transitory computerreadable storage medium; program code, stored on non-transitory computerreadable storage medium, for identifying information about a pluralityof components using the data to form a plurality of instances for aplurality of nodes in response to receiving data for the plurality ofcomponents, wherein each node in the plurality of nodes corresponds to acomponent in the plurality of components; program code, stored on thenon-transitory computer readable storage medium, for forming a number ofvariables having a range of values, in which each value in the range ofvalues indicates a point in time at which a change to the informationfor a particular node occurs between a first instance and a secondinstance in the plurality of instances to form a data structure, whereinmore than one change between two consecutive instances is absent; andprogram code, stored on the non-transitory computer readable storagemedium, for using the data structure to identify probability informationabout the plurality of components associated with the plurality ofnodes, wherein the probability information is calculated such that: eachentry in a conditional probability table of a modified networkcorresponds to a probability that a value of component X changesprecisely at a particular time given that a state of components on whichthe state of the component X depends, the parents, changes at aparticular combination of times, and is defined as follows: aprobability that a change in the state of the component X which isobserved to certainly occur between time t₀ and time t₁ occurs preciselyat time step v, conditioned on the state of the parents changing theirstate precisely at time steps U₁, U₂, . . . , U_(L) is given by aproduct of a number of negative elementary transition probabilities, onefor each time up to and including v, and a positive elementarytransition probability from time t₀ to time t₁ at time step (v+1); suchthat a negative elementary transition probability for time t correspondsto the probability, of a presumed state of the parents failing to inducea state change in component X, and is retrieved from a correspondingconditional probability table of an original Bayesian network datastructure, where the corresponding conditional probability table isdetermined by the state of the parents that the parents would have attime t had the changes in their state occurred precisely at time stepsU₁, U₂, . . . , U_(L) and the state of component X being a value at abeginning of an interval in which the state change in component X isknown to have occurred; and such that a positive elementary transitionprobability for time t corresponds to the probability of the presumedstate of the parents, inducing the change in the state of component X,and is retrieved from the corresponding conditional probability table ofthe original Bayesian network data structure, where the correspondingconditional probability table is determined by the state of the parentsthat the parents would have at time t had the changes in their stateoccurred precisely at time steps U₁, U₂, . . . , U_(L) and the state ofcomponent X being the value at an end of an interval in which the changeis known to have occurred.
 24. An apparatus for managing a manufacturingsystem comprising: a monitoring system configured to monitor componentsin the manufacturing system, wherein the components include monitoredcomponents and unmonitored components and to generate data for themonitored components; and a processor unit in communication with themonitoring system and configured to receive the data for the monitoredcomponents; identify information about the monitored components usingthe data, in response to receiving the data for the monitoredcomponents, to form a plurality of instances for a plurality of nodes inwhich each node in the plurality of nodes corresponds to a monitoredcomponent in the monitored components; form a number of variables havinga range of values, in which each value in the range of values indicatesa point in time at which a change to the information for a particularnode occurs between a first instance and a second instance in theplurality of instances to form a data structure, wherein more than onechange between two consecutive instances is absent; identify probabilityinformation about the components in the system; and initiate anoperation to be performed for the manufacturing system based on theprobability information, wherein the probability information comprisesprobabilities of when a state of a component X in the plurality ofcomponents changes based on when a number of states for a number ofcomponents in the plurality of components changes, calculated such that:each entry in a conditional probability table of a modified networkcorresponds to a probability that a value of component X changesprecisely at a particular time given that a state of components on whichthe state of the component X depends, the parents, changes at aparticular combination of times, and is defined as follows: aprobability that a change in the state of the component X which isobserved to certainly occur between time t₀ and time t₁ occurs preciselyat time step v, conditioned on the state of the parents changing theirstate precisely at time steps U₁, U₂, . . . , U_(L) is given by aproduct of a number of negative elementary transition probabilities, onefor each time up to and including v, and a positive elementarytransition probability from time t₀ to time t₁ at time step (v+1); suchthat a negative elementary transition probability for time t correspondsto the probability, of a presumed state of the parents failing to inducea state change in component X, and is retrieved from a correspondingconditional probability table of an original Bayesian network datastructure, where the corresponding conditional probability table isdetermined by the state of the parents that the parents would have attime t had the changes in their state occurred precisely at time stepsU₁, U₂, . . . , U_(L) and the state of component X being a value at abeginning of an interval in which the state change in component X isknown to have occurred; and such that a positive elementary transitionprobability for time t corresponds to the probability of the presumedstate of the parents, inducing the change in the state of component X,and is retrieved from the corresponding conditional probability table ofthe original Bayesian network data structure, where the correspondingconditional probability table is determined by the state of the parentsthat the parents would have at time t had the changes in their stateoccurred precisely at time steps U₁, U₂, . . . , U_(L) and the state ofcomponent X being the value at an end of an interval in which the changeis known to have occurred.
 25. The apparatus of claim 24, wherein thechange to the information for the node is a change to at least one of:state information, health information, maintenance data, diagnosticdata, sensor data, a file, a report, and a log for a component to whichthe node corresponds.
 26. The apparatus of claim 24, wherein theprocessor unit is further configured to execute a program code to form afirst data structure using the plurality of nodes and the plurality ofinstances; and form the data structure using the number of variableshaving the range of values, wherein the data structure is a second datastructure.
 27. The apparatus of claim 24, wherein the processor unit isfurther configured to form a first data structure using the plurality ofnodes and the plurality of instances; and form the data structure usingthe number of variables having the range of values, wherein the datastructure is a second data structure.
 28. The apparatus of claim 27,wherein the first data structure is a graphical model and the seconddata structure is a transformed graphical model.
 29. A method formanaging components in a manufacturing system, the method comprising:monitoring components in the manufacturing system, wherein thecomponents that are monitored are monitored components; generating datafor the monitored components; receiving the data for the monitoredcomponents; identifying information about the monitored components usingthe data, in response to receiving the data, to form a plurality ofinstances for a plurality of nodes for the monitored components in whicheach node in the plurality of nodes corresponds to a monitored componentin the monitored components; forming a number of variables having arange of values, in which each value in the range of values indicates apoint in time at which a change to the information for a node occursbetween a first instance and a second instance in the plurality ofinstances to form a data structure, wherein more than one change betweentwo consecutive instances is absent; identifying probability informationabout the components in the manufacturing system; identifying at leastone of present and future states for at least one of the monitoredcomponents and unmonitored components in the manufacturing system basedon the probability information; and initiating an operation to beperformed on the manufacturing system in response to identifying the atleast one of the monitored components and unmonitored components in themanufacturing system based on the probability information, wherein theprobability information comprises probabilities of when a state of acomponent X in the plurality of components changes based on when anumber of states for a number of components in the plurality ofcomponents changes, calculated such that: each entry in a conditionalprobability table of a modified network corresponds to a probabilitythat a value of component X changes precisely at a particular time giventhat a state of components on which the state of the component Xdepends, the parents, changes at a particular combination of times, andis defined as follows: a probability that a change in the state of thecomponent X which is observed to certainly occur between time t₀ andtime t₁ occurs precisely at time step v, conditioned on the state of theparents changing their state precisely at time steps U₁, U₂, . . . ,U_(L) is given by a product of a number of negative elementarytransition probabilities, one for each time up to and including v, and apositive elementary transition probability from time t₀ to time t₁ attime step (v+1); such that a negative elementary transition probabilityfor time t corresponds to the probability, of a presumed state of theparents failing to induce a state change in component X, and isretrieved from a corresponding conditional probability table of anoriginal Bayesian network data structure, where the correspondingconditional probability table is determined by the state of the parentsthat the parents would have at time t had the changes in their stateoccurred precisely at time steps U₁, U₂, . . . , U_(L) and the state ofcomponent X being a value at a beginning of an interval in which thestate change in component X is known to have occurred; and such that apositive elementary transition probability for time t corresponds to theprobability of the presumed state of the parents, inducing the change inthe state of component X, and is retrieved from the correspondingconditional probability table of the original Bayesian network datastructure, where the corresponding conditional probability table isdetermined by the state of the parents that the parents would have attime t had the changes in their state occurred precisely at time stepsU₁, U₂, . . . , U_(L) and the state of component X being the value at anend of an interval in which the change is known to have occurred. 30.The method of claim 29, wherein the change to the information for thenode is a change to at least one of state information, healthinformation, maintenance data, diagnostic data, sensor data, a file, areport, and a log for a component to which the node corresponds.